Overview

Dataset statistics

Number of variables29
Number of observations111733
Missing cells5173
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.7 MiB
Average record size in memory232.0 B

Variable types

Numeric10
Text3
Categorical16

Alerts

ID is highly overall correlated with DaysSinceCreationHigh correlation
DaysSinceCreation is highly overall correlated with IDHigh correlation
AverageLeadTime is highly overall correlated with LodgingRevenue and 4 other fieldsHigh correlation
LodgingRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
OtherRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
BookingsCheckedIn is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
PersonsNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
RoomNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
DistributionChannel is highly overall correlated with MarketSegmentHigh correlation
MarketSegment is highly overall correlated with DistributionChannelHigh correlation
BookingsNoShowed is highly imbalanced (99.7%)Imbalance
DistributionChannel is highly imbalanced (57.8%)Imbalance
SRHighFloor is highly imbalanced (74.6%)Imbalance
SRLowFloor is highly imbalanced (98.6%)Imbalance
SRAccessibleRoom is highly imbalanced (99.7%)Imbalance
SRMediumFloor is highly imbalanced (99.1%)Imbalance
SRBathtub is highly imbalanced (96.9%)Imbalance
SRShower is highly imbalanced (98.3%)Imbalance
SRCrib is highly imbalanced (88.1%)Imbalance
SRNearElevator is highly imbalanced (99.6%)Imbalance
SRAwayFromElevator is highly imbalanced (96.6%)Imbalance
SRNoAlcoholInMiniBar is highly imbalanced (99.7%)Imbalance
SRQuietRoom is highly imbalanced (57.1%)Imbalance
Age has 4172 (3.7%) missing valuesMissing
BookingsCanceled is highly skewed (γ1 = 84.06919629)Skewed
BookingsCheckedIn is highly skewed (γ1 = 26.42580106)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique
AverageLeadTime has 36678 (32.8%) zerosZeros
LodgingRevenue has 33769 (30.2%) zerosZeros
OtherRevenue has 33552 (30.0%) zerosZeros
BookingsCanceled has 111567 (99.9%) zerosZeros
BookingsCheckedIn has 33198 (29.7%) zerosZeros
PersonsNights has 33202 (29.7%) zerosZeros
RoomNights has 33198 (29.7%) zerosZeros

Reproduction

Analysis started2024-02-28 13:48:05.167191
Analysis finished2024-02-28 13:48:20.855794
Duration15.69 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct111733
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55867
Minimum1
Maximum111733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:20.938553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5587.6
Q127934
median55867
Q383800
95-th percentile106146.4
Maximum111733
Range111732
Interquartile range (IQR)55866

Descriptive statistics

Standard deviation32254.683
Coefficient of variation (CV)0.57734769
Kurtosis-1.2
Mean55867
Median Absolute Deviation (MAD)27933
Skewness0
Sum6.2421875 × 109
Variance1.0403646 × 109
MonotonicityStrictly increasing
2024-02-28T13:48:21.043194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
74487 1
 
< 0.1%
74498 1
 
< 0.1%
74497 1
 
< 0.1%
74496 1
 
< 0.1%
74495 1
 
< 0.1%
74494 1
 
< 0.1%
74493 1
 
< 0.1%
74492 1
 
< 0.1%
74491 1
 
< 0.1%
Other values (111723) 111723
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
111733 1
< 0.1%
111732 1
< 0.1%
111731 1
< 0.1%
111730 1
< 0.1%
111729 1
< 0.1%
111728 1
< 0.1%
111727 1
< 0.1%
111726 1
< 0.1%
111725 1
< 0.1%
111724 1
< 0.1%
Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:21.204661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters335199
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowDEU
4th rowFRA
5th rowFRA
ValueCountFrequency (%)
fra 16516
14.8%
deu 14805
13.3%
prt 14101
12.6%
gbr 11462
10.3%
esp 6123
 
5.5%
usa 5409
 
4.8%
ita 4268
 
3.8%
bel 4111
 
3.7%
bra 4037
 
3.6%
nld 3794
 
3.4%
Other values (189) 27107
24.3%
2024-02-28T13:48:21.531359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 335199
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 335199
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 54928
16.4%
A 36750
11.0%
E 30432
9.1%
U 25951
 
7.7%
P 21940
 
6.5%
T 21072
 
6.3%
B 20220
 
6.0%
D 19948
 
6.0%
F 17548
 
5.2%
S 16964
 
5.1%
Other values (16) 69446
20.7%

Age
Real number (ℝ)

MISSING 

Distinct106
Distinct (%)0.1%
Missing4172
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean45.639191
Minimum-10
Maximum123
Zeros18
Zeros (%)< 0.1%
Negative14
Negative (%)< 0.1%
Memory size873.0 KiB
2024-02-28T13:48:21.639309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-10
5-th percentile15
Q133
median47
Q358
95-th percentile73
Maximum123
Range133
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.244952
Coefficient of variation (CV)0.37785403
Kurtosis-0.37626353
Mean45.639191
Median Absolute Deviation (MAD)12
Skewness-0.15306479
Sum4908997
Variance297.38838
MonotonicityNot monotonic
2024-02-28T13:48:21.748943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 2582
 
2.3%
52 2561
 
2.3%
55 2557
 
2.3%
50 2504
 
2.2%
54 2493
 
2.2%
48 2458
 
2.2%
49 2441
 
2.2%
53 2430
 
2.2%
56 2360
 
2.1%
47 2278
 
2.0%
Other values (96) 82897
74.2%
(Missing) 4172
 
3.7%
ValueCountFrequency (%)
-10 2
 
< 0.1%
-9 4
 
< 0.1%
-8 2
 
< 0.1%
-6 3
 
< 0.1%
-5 3
 
< 0.1%
0 18
 
< 0.1%
1 141
0.1%
2 231
0.2%
3 193
0.2%
4 217
0.2%
ValueCountFrequency (%)
123 1
 
< 0.1%
115 2
< 0.1%
114 3
< 0.1%
111 2
< 0.1%
110 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
93 4
< 0.1%
92 2
< 0.1%
91 3
< 0.1%

DaysSinceCreation
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.0266
Minimum36
Maximum1385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:21.854590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile80
Q1288
median522
Q3889
95-th percentile1263.4
Maximum1385
Range1349
Interquartile range (IQR)601

Descriptive statistics

Standard deviation374.65738
Coefficient of variation (CV)0.62964812
Kurtosis-0.97159451
Mean595.0266
Median Absolute Deviation (MAD)295
Skewness0.39956274
Sum66484107
Variance140368.15
MonotonicityNot monotonic
2024-02-28T13:48:21.965221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 298
 
0.3%
522 247
 
0.2%
108 234
 
0.2%
312 234
 
0.2%
137 232
 
0.2%
571 227
 
0.2%
391 225
 
0.2%
485 220
 
0.2%
368 211
 
0.2%
507 211
 
0.2%
Other values (1339) 109394
97.9%
ValueCountFrequency (%)
36 6
 
< 0.1%
37 109
0.1%
38 68
 
0.1%
39 200
0.2%
40 113
0.1%
41 140
0.1%
42 141
0.1%
43 101
0.1%
44 106
0.1%
45 98
0.1%
ValueCountFrequency (%)
1385 70
0.1%
1384 90
0.1%
1383 103
0.1%
1382 16
 
< 0.1%
1381 99
0.1%
1380 21
 
< 0.1%
1379 10
 
< 0.1%
1378 15
 
< 0.1%
1377 5
 
< 0.1%
1376 20
 
< 0.1%
Distinct107584
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:22.089807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters7374378
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique104505 ?
Unique (%)93.5%

Sample

1st row0x2C371FD6CE12936774A139FD7430C624F1C4D5109CE64F21BF4BEB13F3C2641B
2nd row0x198CDB98BF37B6E23F9548C56A88B00912D65A9AA0D628E8365F7117727FB9C3
3rd row0xDA46E62F66936284DF2844EC4FC542D0DAD780C0EE0C00C8CE9BD139A6B912DF
4th row0xC45D4CD22C58FDC5FD0F95315F6EFA5A6E7149187D493EE28BA4BFDB7E2A3EC3
5th row0xD2E3D5BFCA141865669F98D64CDA85AD04DEFF47F8A0C6EC48B7D587EDAE6F53
ValueCountFrequency (%)
0x15a713ce687991691a18f6cdc56abe24979c73cf5d51ef134b078b9d61a8cc4a 75
 
0.1%
0xf1465ec9e2d0027094b7c3d72772a862a635e461fffc9aa009c2bd78d31bbb63 30
 
< 0.1%
0x8df2af984365949e7f4eab2eba9bf9ca8df106b5f2a960ad7d9a7fb4ec61d02f 21
 
< 0.1%
0xd32ff3d74c193ef22762853a9f3dfae05172a9537cca78a8d519372eb35bb910 20
 
< 0.1%
0xfb64b4b6ab53a6a549a620009ca24a1c3a668a460d8787f8c7a413529295fd50 15
 
< 0.1%
0x5d6d35b2a085c783fb5eaa6088c9c97ee3596f2fb552e3f86410b2b9b91a0350 13
 
< 0.1%
0xcc6a9be603d312defdfd886218fb1fe35ec93c47f6c755cafd68308cead6610b 11
 
< 0.1%
0x01405047149a1fa594108cc41d265066a6e7bfafd6491af690f4402cf9802119 11
 
< 0.1%
0x57875df029939f73857e321cf04081870e118531ce2914aa30de15584b1d8511 11
 
< 0.1%
0x36bf5a087e8029783fc52501a957412690aa36e271ce20435600825a54909317 10
 
< 0.1%
Other values (107574) 111516
99.8%
2024-02-28T13:48:22.282070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 558041
 
7.6%
9 448157
 
6.1%
3 447793
 
6.1%
1 447685
 
6.1%
5 447641
 
6.1%
8 447562
 
6.1%
E 447385
 
6.1%
A 447277
 
6.1%
7 446888
 
6.1%
B 446836
 
6.1%
Other values (7) 2789113
37.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4582219
62.1%
Uppercase Letter 2680426
36.3%
Lowercase Letter 111733
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 558041
12.2%
9 448157
9.8%
3 447793
9.8%
1 447685
9.8%
5 447641
9.8%
8 447562
9.8%
7 446888
9.8%
2 446250
9.7%
4 446144
9.7%
6 446058
9.7%
Uppercase Letter
ValueCountFrequency (%)
E 447385
16.7%
A 447277
16.7%
B 446836
16.7%
F 446741
16.7%
D 446230
16.6%
C 445957
16.6%
Lowercase Letter
ValueCountFrequency (%)
x 111733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4582219
62.1%
Latin 2792159
37.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 558041
12.2%
9 448157
9.8%
3 447793
9.8%
1 447685
9.8%
5 447641
9.8%
8 447562
9.8%
7 446888
9.8%
2 446250
9.7%
4 446144
9.7%
6 446058
9.7%
Latin
ValueCountFrequency (%)
E 447385
16.0%
A 447277
16.0%
B 446836
16.0%
F 446741
16.0%
D 446230
16.0%
C 445957
16.0%
x 111733
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7374378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 558041
 
7.6%
9 448157
 
6.1%
3 447793
 
6.1%
1 447685
 
6.1%
5 447641
 
6.1%
8 447562
 
6.1%
E 447385
 
6.1%
A 447277
 
6.1%
7 446888
 
6.1%
B 446836
 
6.1%
Other values (7) 2789113
37.8%
Distinct103480
Distinct (%)93.5%
Missing1001
Missing (%)0.9%
Memory size873.0 KiB
2024-02-28T13:48:22.395586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters7308312
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100395 ?
Unique (%)90.7%

Sample

1st row0x434FD3D59469C73AFEA087017FAF8CA2296493AEABDE035E6860ACB8C43FCEE5
2nd row0xE3B0C44298FC1C149AFBF4C8996FB92427AE41E4649B934CA495991B7852B855
3rd row0x27F5DF762CCDA622C752CCDA45794923BED9F1B6630098203B1DDE7A89E03DC2
4th row0x8E59572913BB9B1E6CAA12FA2C8B7BF387B1D1F3432E65A6EF9AA426D1C5E8EF
5th row0x42BDEE0E05A9441C94147076EDDCC47E604DA5447DD4BA9B4D23642E66E82F99
ValueCountFrequency (%)
0xe3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 3032
 
2.7%
0xa486fbacf4b4e5537b026743e3fdfe571d716839e758236f42950a61fe6b922b 31
 
< 0.1%
0x2b17e9d2ccef2ea0fe752ee345bedfb06741ffc8ececf45d6bbdbaf9a274ff52 24
 
< 0.1%
0x469cf1f9cf8c790ffa5ad3f484f2938cbeff6435bcfd734f687ec6d1e968f076 15
 
< 0.1%
0x2a14d03a4827c67e0d39408f103db417ad496dce6158f8309e6281185c042003 14
 
< 0.1%
0x9220d336f2ddd7b68f5066878889c7637ee28924b249f968f5ec82d895b108a7 12
 
< 0.1%
0x3856085146f7bc27bd07bfc4ca1991ed4e65e179d7bdb7dbba7e32620809c799 12
 
< 0.1%
0xd2dbd6039916f6db10c6564d8eb9a9116811435965d7d00e7da292066b3ece91 11
 
< 0.1%
0x1bf60c4718497a0ab8b46ff00708d3250a484dda0fdc0248999c782807195bcb 11
 
< 0.1%
0x6b421376b94f3d1722979458a96df486dea0f9290cc05e9699f2762fd0dda71d 10
 
< 0.1%
Other values (103470) 107560
97.1%
2024-02-28T13:48:22.581036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 544910
 
7.5%
4 461289
 
6.3%
9 461274
 
6.3%
B 447978
 
6.1%
C 446417
 
6.1%
F 444237
 
6.1%
1 443281
 
6.1%
8 443053
 
6.1%
2 442696
 
6.1%
5 442098
 
6.0%
Other values (7) 2731079
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4547315
62.2%
Uppercase Letter 2650265
36.3%
Lowercase Letter 110732
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 544910
12.0%
4 461289
10.1%
9 461274
10.1%
1 443281
9.7%
8 443053
9.7%
2 442696
9.7%
5 442098
9.7%
6 436661
9.6%
7 436515
9.6%
3 435538
9.6%
Uppercase Letter
ValueCountFrequency (%)
B 447978
16.9%
C 446417
16.8%
F 444237
16.8%
E 440617
16.6%
A 439294
16.6%
D 431722
16.3%
Lowercase Letter
ValueCountFrequency (%)
x 110732
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4547315
62.2%
Latin 2760997
37.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 544910
12.0%
4 461289
10.1%
9 461274
10.1%
1 443281
9.7%
8 443053
9.7%
2 442696
9.7%
5 442098
9.7%
6 436661
9.6%
7 436515
9.6%
3 435538
9.6%
Latin
ValueCountFrequency (%)
B 447978
16.2%
C 446417
16.2%
F 444237
16.1%
E 440617
16.0%
A 439294
15.9%
D 431722
15.6%
x 110732
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7308312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 544910
 
7.5%
4 461289
 
6.3%
9 461274
 
6.3%
B 447978
 
6.1%
C 446417
 
6.1%
F 444237
 
6.1%
1 443281
 
6.1%
8 443053
 
6.1%
2 442696
 
6.1%
5 442098
 
6.0%
Other values (7) 2731079
37.4%

AverageLeadTime
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct424
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.833147
Minimum-1
Maximum588
Zeros36678
Zeros (%)32.8%
Negative13
Negative (%)< 0.1%
Memory size873.0 KiB
2024-02-28T13:48:22.673740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median21
Q395
95-th percentile236
Maximum588
Range589
Interquartile range (IQR)95

Descriptive statistics

Standard deviation85.11532
Coefficient of variation (CV)1.3991602
Kurtosis4.3838783
Mean60.833147
Median Absolute Deviation (MAD)21
Skewness1.9213556
Sum6797070
Variance7244.6177
MonotonicityNot monotonic
2024-02-28T13:48:22.771397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36678
32.8%
1 2121
 
1.9%
2 1271
 
1.1%
6 1257
 
1.1%
4 1224
 
1.1%
3 1193
 
1.1%
5 1191
 
1.1%
7 1160
 
1.0%
8 1083
 
1.0%
9 823
 
0.7%
Other values (414) 63732
57.0%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 36678
32.8%
1 2121
 
1.9%
2 1271
 
1.1%
3 1193
 
1.1%
4 1224
 
1.1%
5 1191
 
1.1%
6 1257
 
1.1%
7 1160
 
1.0%
8 1083
 
1.0%
ValueCountFrequency (%)
588 19
< 0.1%
574 10
< 0.1%
549 22
< 0.1%
546 10
< 0.1%
543 2
 
< 0.1%
542 5
 
< 0.1%
541 5
 
< 0.1%
535 22
< 0.1%
534 1
 
< 0.1%
533 2
 
< 0.1%

LodgingRevenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12689
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.85128
Minimum0
Maximum21781
Zeros33769
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:22.871062image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median208
Q3393.3
95-th percentile882
Maximum21781
Range21781
Interquartile range (IQR)393.3

Descriptive statistics

Standard deviation379.13156
Coefficient of variation (CV)1.3356697
Kurtosis149.30816
Mean283.85128
Median Absolute Deviation (MAD)208
Skewness6.1687755
Sum31715555
Variance143740.74
MonotonicityNot monotonic
2024-02-28T13:48:22.966755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33769
30.2%
176 988
 
0.9%
126 673
 
0.6%
234 592
 
0.5%
264 551
 
0.5%
249 525
 
0.5%
168 489
 
0.4%
89 405
 
0.4%
178 331
 
0.3%
210 325
 
0.3%
Other values (12679) 73085
65.4%
ValueCountFrequency (%)
0 33769
30.2%
18 2
 
< 0.1%
22 1
 
< 0.1%
24 5
 
< 0.1%
25 1
 
< 0.1%
28 1
 
< 0.1%
34 4
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
21781 1
< 0.1%
14044.8 1
< 0.1%
9682.4 1
< 0.1%
9665.66 1
< 0.1%
9180 1
< 0.1%
9010 1
< 0.1%
8493.65 1
< 0.1%
7902 1
< 0.1%
7458 1
< 0.1%
7256 1
< 0.1%

OtherRevenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5338
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.682802
Minimum0
Maximum8859.25
Zeros33552
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:23.060436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q384
95-th percentile235
Maximum8859.25
Range8859.25
Interquartile range (IQR)84

Descriptive statistics

Standard deviation123.58071
Coefficient of variation (CV)1.9105653
Kurtosis578.66804
Mean64.682802
Median Absolute Deviation (MAD)31
Skewness14.895075
Sum7227203.5
Variance15272.193
MonotonicityNot monotonic
2024-02-28T13:48:23.157115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33552
30.0%
42 3030
 
2.7%
14 2913
 
2.6%
28 2328
 
2.1%
56 1723
 
1.5%
7 1717
 
1.5%
21 1311
 
1.2%
16 1120
 
1.0%
2 1089
 
1.0%
8 1045
 
0.9%
Other values (5328) 61905
55.4%
ValueCountFrequency (%)
0 33552
30.0%
1 370
 
0.3%
1.9 1
 
< 0.1%
2 1089
 
1.0%
2.1 3
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 3
 
< 0.1%
3 184
 
0.2%
3.24 1
 
< 0.1%
ValueCountFrequency (%)
8859.25 1
 
< 0.1%
5268.5 1
 
< 0.1%
5261 1
 
< 0.1%
5237 7
< 0.1%
5105.5 1
 
< 0.1%
4296 1
 
< 0.1%
3692.4 1
 
< 0.1%
3580.5 1
 
< 0.1%
3190.4 1
 
< 0.1%
3050.85 1
 
< 0.1%

BookingsCanceled
Real number (ℝ)

SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0022822264
Minimum0
Maximum15
Zeros111567
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:23.234885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08063146
Coefficient of variation (CV)35.330176
Kurtosis12061.368
Mean0.0022822264
Median Absolute Deviation (MAD)0
Skewness84.069196
Sum255
Variance0.0065014323
MonotonicityNot monotonic
2024-02-28T13:48:23.312626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 111567
99.9%
1 125
 
0.1%
2 19
 
< 0.1%
3 11
 
< 0.1%
4 8
 
< 0.1%
15 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 111567
99.9%
1 125
 
0.1%
2 19
 
< 0.1%
3 11
 
< 0.1%
4 8
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
7 1
 
< 0.1%
5 1
 
< 0.1%
4 8
 
< 0.1%
3 11
 
< 0.1%
2 19
 
< 0.1%
1 125
 
0.1%
0 111567
99.9%

BookingsNoShowed
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111676 
1
 
48
2
 
8
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Length

2024-02-28T13:48:23.399332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:23.474246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111676
99.9%
1 48
 
< 0.1%
2 8
 
< 0.1%
3 1
 
< 0.1%

BookingsCheckedIn
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73760662
Minimum0
Maximum76
Zeros33198
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:23.551974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73088936
Coefficient of variation (CV)0.99089317
Kurtosis1943.6126
Mean0.73760662
Median Absolute Deviation (MAD)0
Skewness26.425801
Sum82415
Variance0.53419925
MonotonicityNot monotonic
2024-02-28T13:48:23.646665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 76474
68.4%
0 33198
29.7%
2 1634
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
7 30
 
< 0.1%
6 19
 
< 0.1%
9 13
 
< 0.1%
8 12
 
< 0.1%
Other values (23) 62
 
0.1%
ValueCountFrequency (%)
0 33198
29.7%
1 76474
68.4%
2 1634
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
6 19
 
< 0.1%
7 30
 
< 0.1%
8 12
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
66 1
< 0.1%
40 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
29 2
< 0.1%
26 2
< 0.1%
25 1
< 0.1%
24 1
< 0.1%

PersonsNights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3283184
Minimum0
Maximum116
Zeros33202
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:23.745300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile12
Maximum116
Range116
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6307386
Coefficient of variation (CV)1.0698701
Kurtosis12.871917
Mean4.3283184
Median Absolute Deviation (MAD)4
Skewness2.0002975
Sum483616
Variance21.44374
MonotonicityNot monotonic
2024-02-28T13:48:23.931671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33202
29.7%
6 16328
14.6%
4 12926
 
11.6%
2 11815
 
10.6%
8 10245
 
9.2%
1 5147
 
4.6%
3 4964
 
4.4%
10 4296
 
3.8%
12 3931
 
3.5%
9 2253
 
2.0%
Other values (50) 6626
 
5.9%
ValueCountFrequency (%)
0 33202
29.7%
1 5147
 
4.6%
2 11815
 
10.6%
3 4964
 
4.4%
4 12926
 
11.6%
5 1116
 
1.0%
6 16328
14.6%
7 291
 
0.3%
8 10245
 
9.2%
9 2253
 
2.0%
ValueCountFrequency (%)
116 1
< 0.1%
99 1
< 0.1%
91 1
< 0.1%
80 1
< 0.1%
75 1
< 0.1%
68 2
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%

RoomNights
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2038252
Minimum0
Maximum185
Zeros33198
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size873.0 KiB
2024-02-28T13:48:24.031340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum185
Range185
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3016373
Coefficient of variation (CV)1.0443829
Kurtosis489.93256
Mean2.2038252
Median Absolute Deviation (MAD)2
Skewness9.1896303
Sum246240
Variance5.2975341
MonotonicityNot monotonic
2024-02-28T13:48:24.134559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 33198
29.7%
3 20706
18.5%
2 17484
15.6%
4 14050
12.6%
1 13665
12.2%
5 6248
 
5.6%
7 2570
 
2.3%
6 2424
 
2.2%
8 505
 
0.5%
9 271
 
0.2%
Other values (39) 612
 
0.5%
ValueCountFrequency (%)
0 33198
29.7%
1 13665
12.2%
2 17484
15.6%
3 20706
18.5%
4 14050
12.6%
5 6248
 
5.6%
6 2424
 
2.2%
7 2570
 
2.3%
8 505
 
0.5%
9 271
 
0.2%
ValueCountFrequency (%)
185 1
< 0.1%
116 1
< 0.1%
95 1
< 0.1%
88 2
< 0.1%
59 1
< 0.1%
51 2
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
42 2
< 0.1%
40 2
< 0.1%

DistributionChannel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
Travel Agent/Operator
91019 
Direct
16883 
Corporate
 
3135
GDS Systems
 
696

Length

Max length21
Median length21
Mean length18.334494
Min length6

Characters and Unicode

Total characters2048568
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Travel Agent/Operator 91019
81.5%
Direct 16883
 
15.1%
Corporate 3135
 
2.8%
GDS Systems 696
 
0.6%

Length

2024-02-28T13:48:24.234221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:24.310966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
travel 91019
44.7%
agent/operator 91019
44.7%
direct 16883
 
8.3%
corporate 3135
 
1.5%
gds 696
 
0.3%
systems 696
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1569975
76.6%
Uppercase Letter 295859
 
14.4%
Space Separator 91715
 
4.5%
Other Punctuation 91019
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 296210
18.9%
e 293771
18.7%
t 202752
12.9%
a 185173
11.8%
o 97289
 
6.2%
p 94154
 
6.0%
n 91019
 
5.8%
g 91019
 
5.8%
l 91019
 
5.8%
v 91019
 
5.8%
Other values (5) 36550
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
T 91019
30.8%
O 91019
30.8%
A 91019
30.8%
D 17579
 
5.9%
C 3135
 
1.1%
S 1392
 
0.5%
G 696
 
0.2%
Space Separator
ValueCountFrequency (%)
91715
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 91019
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1865834
91.1%
Common 182734
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 296210
15.9%
e 293771
15.7%
t 202752
10.9%
a 185173
9.9%
o 97289
 
5.2%
p 94154
 
5.0%
T 91019
 
4.9%
O 91019
 
4.9%
n 91019
 
4.9%
g 91019
 
4.9%
Other values (12) 332409
17.8%
Common
ValueCountFrequency (%)
91715
50.2%
/ 91019
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2048568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 296210
14.5%
e 293771
14.3%
t 202752
 
9.9%
a 185173
 
9.0%
o 97289
 
4.7%
p 94154
 
4.6%
91715
 
4.5%
T 91019
 
4.4%
/ 91019
 
4.4%
O 91019
 
4.4%
Other values (14) 514447
25.1%

MarketSegment
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
Other
63680 
Direct
16363 
Travel Agent/Operator
16353 
Groups
11461 
Corporate
 
2931
Other values (2)
 
945

Length

Max length21
Median length5
Mean length7.7504497
Min length5

Characters and Unicode

Total characters865981
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Other 63680
57.0%
Direct 16363
 
14.6%
Travel Agent/Operator 16353
 
14.6%
Groups 11461
 
10.3%
Corporate 2931
 
2.6%
Complementary 657
 
0.6%
Aviation 288
 
0.3%

Length

2024-02-28T13:48:24.398679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:24.483395image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
other 63680
49.7%
direct 16363
 
12.8%
travel 16353
 
12.8%
agent/operator 16353
 
12.8%
groups 11461
 
8.9%
corporate 2931
 
2.3%
complementary 657
 
0.5%
aviation 288
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 688836
79.5%
Uppercase Letter 144439
 
16.7%
Other Punctuation 16353
 
1.9%
Space Separator 16353
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 147082
21.4%
e 133347
19.4%
t 116625
16.9%
h 63680
9.2%
a 36582
 
5.3%
o 34621
 
5.0%
p 31402
 
4.6%
n 17298
 
2.5%
l 17010
 
2.5%
i 16939
 
2.5%
Other values (7) 74250
10.8%
Uppercase Letter
ValueCountFrequency (%)
O 80033
55.4%
A 16641
 
11.5%
D 16363
 
11.3%
T 16353
 
11.3%
G 11461
 
7.9%
C 3588
 
2.5%
Other Punctuation
ValueCountFrequency (%)
/ 16353
100.0%
Space Separator
ValueCountFrequency (%)
16353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 833275
96.2%
Common 32706
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 147082
17.7%
e 133347
16.0%
t 116625
14.0%
O 80033
9.6%
h 63680
7.6%
a 36582
 
4.4%
o 34621
 
4.2%
p 31402
 
3.8%
n 17298
 
2.1%
l 17010
 
2.0%
Other values (13) 155595
18.7%
Common
ValueCountFrequency (%)
/ 16353
50.0%
16353
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 865981
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 147082
17.0%
e 133347
15.4%
t 116625
13.5%
O 80033
9.2%
h 63680
 
7.4%
a 36582
 
4.2%
o 34621
 
4.0%
p 31402
 
3.6%
n 17298
 
2.0%
l 17010
 
2.0%
Other values (15) 188301
21.7%

SRHighFloor
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
106983 
1
 
4750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Length

2024-02-28T13:48:24.576041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:24.639835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106983
95.7%
1 4750
 
4.3%

SRLowFloor
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111587 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Length

2024-02-28T13:48:24.710629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:24.776412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111587
99.9%
1 146
 
0.1%

SRAccessibleRoom
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111708 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Length

2024-02-28T13:48:24.848482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:24.911246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111708
> 99.9%
1 25
 
< 0.1%

SRMediumFloor
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111647 
1
 
86

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Length

2024-02-28T13:48:24.982704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.049533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111647
99.9%
1 86
 
0.1%

SRBathtub
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111383 
1
 
350

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Length

2024-02-28T13:48:25.121328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.188115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111383
99.7%
1 350
 
0.3%

SRShower
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111551 
1
 
182

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Length

2024-02-28T13:48:25.261863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.328640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111551
99.8%
1 182
 
0.2%

SRCrib
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
109925 
1
 
1808

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Length

2024-02-28T13:48:25.399396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.466176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 109925
98.4%
1 1808
 
1.6%

SRKingSizeBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
71144 
1
40589 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Length

2024-02-28T13:48:25.538929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.608601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring characters

ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71144
63.7%
1 40589
36.3%

SRTwinBed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
94212 
1
17521 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Length

2024-02-28T13:48:25.683445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.752217image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94212
84.3%
1 17521
 
15.7%

SRNearElevator
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111696 
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Length

2024-02-28T13:48:25.824973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:25.891760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111696
> 99.9%
1 37
 
< 0.1%

SRAwayFromElevator
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111331 
1
 
402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Length

2024-02-28T13:48:25.965909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:26.033670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111331
99.6%
1 402
 
0.4%

SRNoAlcoholInMiniBar
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
111711 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Length

2024-02-28T13:48:26.105434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:26.172210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111711
> 99.9%
1 22
 
< 0.1%

SRQuietRoom
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size873.0 KiB
0
101932 
1
 
9801

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters111733
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Length

2024-02-28T13:48:26.244936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T13:48:26.312711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 111733
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 111733
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101932
91.2%
1 9801
 
8.8%

Interactions

2024-02-28T13:48:19.139781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:11.845876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.621785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.488129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.307245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.099275image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.864223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.624581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.535180image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.336460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.218449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:11.922267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.698529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.565900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.383938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.177342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.935043image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.701318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.611892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.408151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.300233image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.003017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.775200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.646632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.464604image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.260691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.007814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.783394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.691554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.487885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.379900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.082730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.862980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.728530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.546193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.341412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.089741image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.865118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.778339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.579577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.460637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.164486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.939724image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.806209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.621917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.416179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.167363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.942844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.856013image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.661310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.533493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.233246image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.022445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.884564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.695704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.484923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.237132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.013888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.931859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.735065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.606143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.305010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.095229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.961653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.770415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.556327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.311080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.101557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.007560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.814911image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.693886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.383969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.253667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.041391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.849544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.636061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.387847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.284976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.090223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.894630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.782622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.464427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.335397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.142120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.930925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.713797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.466551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.368744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.173045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.979317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.859550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:12.541979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:13.411423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:14.222008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.011327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:15.787479image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:16.541850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:17.447469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:18.252748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-02-28T13:48:19.055000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-02-28T13:48:26.389030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
IDAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsCheckedInPersonsNightsRoomNightsBookingsNoShowedDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
ID1.000-0.114-1.000-0.279-0.237-0.307-0.022-0.383-0.314-0.2990.0110.0590.0820.0620.0100.0070.0120.0240.0140.0580.0740.1090.0030.0200.0150.129
Age-0.1141.0000.1140.2300.1170.2120.0120.1810.1470.1520.0050.0690.1210.0260.0150.0060.0020.0240.0130.3760.0470.1350.0230.0140.0100.054
DaysSinceCreation-1.0000.1141.0000.2790.2370.3070.0210.3830.3140.2990.0110.0620.0700.0570.0080.0110.0050.0280.0110.0550.0690.1100.0020.0250.0140.132
AverageLeadTime-0.2790.2300.2791.0000.6860.725-0.0060.7440.7390.7230.0000.0790.1100.0210.0030.0000.0030.0090.0160.0430.0270.1030.0000.0000.0000.032
LodgingRevenue-0.2370.1170.2370.6861.0000.8370.0290.7920.8920.9090.0920.0190.0170.0000.0000.0000.0000.0000.0000.0050.0020.0000.0000.0050.0000.009
OtherRevenue-0.3070.2120.3070.7250.8371.0000.0270.7900.8700.8470.0200.0170.0150.0040.0000.0000.0000.0000.0000.0020.0050.0000.0000.0050.0000.000
BookingsCanceled-0.0220.0120.021-0.0060.0290.0271.0000.0650.0310.0410.2720.0530.0450.0000.0000.0000.0380.0000.0000.0000.0160.0000.0000.0050.0000.000
BookingsCheckedIn-0.3830.1810.3830.7440.7920.7900.0651.0000.7950.8050.3510.0690.0550.0000.0000.0000.0110.0000.0000.0000.0210.0010.0000.0080.0000.000
PersonsNights-0.3140.1470.3140.7390.8920.8700.0310.7951.0000.9520.2630.0330.0450.0110.0000.0000.0000.0130.0050.0120.0300.0230.0000.0100.0000.021
RoomNights-0.2990.1520.2990.7230.9090.8470.0410.8050.9521.0000.3140.0570.0510.0000.0000.0000.0000.0080.0000.0000.0140.0070.0000.0000.0000.000
BookingsNoShowed0.0110.0050.0110.0000.0920.0200.2720.3510.2630.3141.0000.0640.0650.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.000
DistributionChannel0.0590.0690.0620.0790.0190.0170.0530.0690.0330.0570.0641.0000.7190.0350.0070.0000.0170.0270.0280.0480.1560.0930.0060.0230.0000.089
MarketSegment0.0820.1210.0700.1100.0170.0150.0450.0550.0450.0510.0650.7191.0000.1080.0170.0000.0190.0320.0310.0600.3390.1020.0060.0320.0060.215
SRHighFloor0.0620.0260.0570.0210.0000.0040.0000.0000.0110.0000.0000.0350.1081.0000.0050.0000.0020.0280.0180.0000.0860.0060.0140.1310.0000.073
SRLowFloor0.0100.0150.0080.0030.0000.0000.0000.0000.0000.0000.0000.0070.0170.0051.0000.0070.0020.0090.0000.0000.0080.0010.0000.0030.0000.012
SRAccessibleRoom0.0070.0060.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0071.0000.0000.0000.0210.0000.0000.0030.0820.0000.0000.000
SRMediumFloor0.0120.0020.0050.0030.0000.0000.0380.0110.0000.0000.0000.0170.0190.0020.0020.0001.0000.0000.0100.0000.0000.0050.0080.0390.0000.017
SRBathtub0.0240.0240.0280.0090.0000.0000.0000.0000.0130.0080.0000.0270.0320.0280.0090.0000.0001.0000.0000.0280.0280.0060.0000.0050.0000.010
SRShower0.0140.0130.0110.0160.0000.0000.0000.0000.0050.0000.0000.0280.0310.0180.0000.0210.0100.0001.0000.0000.0170.0000.0170.0010.0000.014
SRCrib0.0580.3760.0550.0430.0050.0020.0000.0000.0120.0000.0000.0480.0600.0000.0000.0000.0000.0280.0001.0000.0410.0430.0000.0060.0100.006
SRKingSizeBed0.0740.0470.0690.0270.0020.0050.0160.0210.0300.0140.0120.1560.3390.0860.0080.0000.0000.0280.0170.0411.0000.3120.0000.0140.0040.106
SRTwinBed0.1090.1350.1100.1030.0000.0000.0000.0010.0230.0070.0000.0930.1020.0060.0010.0030.0050.0060.0000.0430.3121.0000.0000.0020.0060.000
SRNearElevator0.0030.0230.0020.0000.0000.0000.0000.0000.0000.0000.0000.0060.0060.0140.0000.0820.0080.0000.0170.0000.0000.0001.0000.0000.0000.000
SRAwayFromElevator0.0200.0140.0250.0000.0050.0050.0050.0080.0100.0000.0000.0230.0320.1310.0030.0000.0390.0050.0010.0060.0140.0020.0001.0000.0000.066
SRNoAlcoholInMiniBar0.0150.0100.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0100.0040.0060.0000.0001.0000.012
SRQuietRoom0.1290.0540.1320.0320.0090.0000.0000.0000.0210.0000.0000.0890.2150.0730.0120.0000.0170.0100.0140.0060.1060.0000.0000.0660.0121.000

Missing values

2024-02-28T13:48:20.006059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T13:48:20.399696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-28T13:48:20.739181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDNationalityAgeDaysSinceCreationNameHashDocIDHashAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
01PRT52.04400x2C371FD6CE12936774A139FD7430C624F1C4D5109CE64F21BF4BEB13F3C2641B0x434FD3D59469C73AFEA087017FAF8CA2296493AEABDE035E6860ACB8C43FCEE559292.082.310264CorporateCorporate0000000000000
12PRTNaN13850x198CDB98BF37B6E23F9548C56A88B00912D65A9AA0D628E8365F7117727FB9C30xE3B0C44298FC1C149AFBF4C8996FB92427AE41E4649B934CA495991B7852B85561280.053.0001105Travel Agent/OperatorTravel Agent/Operator0000000000000
23DEU32.013850xDA46E62F66936284DF2844EC4FC542D0DAD780C0EE0C00C8CE9BD139A6B912DF0x27F5DF762CCDA622C752CCDA45794923BED9F1B6630098203B1DDE7A89E03DC200.00.000000Travel Agent/OperatorTravel Agent/Operator0000000000000
34FRA61.013850xC45D4CD22C58FDC5FD0F95315F6EFA5A6E7149187D493EE28BA4BFDB7E2A3EC30x8E59572913BB9B1E6CAA12FA2C8B7BF387B1D1F3432E65A6EF9AA426D1C5E8EF93240.060.0001105Travel Agent/OperatorTravel Agent/Operator0000000000000
45FRA52.013850xD2E3D5BFCA141865669F98D64CDA85AD04DEFF47F8A0C6EC48B7D587EDAE6F530x42BDEE0E05A9441C94147076EDDCC47E604DA5447DD4BA9B4D23642E66E82F9900.00.000000Travel Agent/OperatorTravel Agent/Operator0000000000000
56JPN55.013850xA3CF1A4692BE0A17CFD3BFD9C07653556BDADF5F4BE7BFC2AA04178085ADB7BE0x506065FBCE220DCEA4465C7310A84F04165BCB5906DCDB4AFF88124A32EC08BC58230.024.000142Travel Agent/OperatorOther0000000000000
67JPN50.013850x94DB830C90A6DA2331968CFC9448AB9A3CE07D7CFEDD0CE6DDA11964EDA49E280x47E5E4B21585F1FD956C768E730604241B380EDFEA68B1C8739EB25934A7849F00.00.000000Travel Agent/OperatorOther0000000000000
78FRA33.013850x165B609162C92BF563E96DB03539363F07E784C219A857DB164E7F8FF7806E910x6BB66BA80C726B9967988A889D83699B609D11C65AD74D2F13A686767B7577CD38535.094.0001105Travel Agent/OperatorOther0000000100000
89FRA43.013850x44BB41EF2D87698E75B6FBB77A8815BF48DAA912C1408CF203221EA1FAEE5B680x6C456E45A78A20BC794137AE326A81D587B6528B39441FA0828F2C59F8FB9E0600.00.000000Travel Agent/OperatorOther0000000100000
910IRL26.013850x9BEECEE0C18B0957C7424443643948E99A0EC8326EF93EFE17A527E1E2AF819A0x199C61A5442D08987001E170B74D244DF6AF1FC9AE92F21B1890DA88921028B596174.069.000163Travel Agent/OperatorTravel Agent/Operator0000000000000
IDNationalityAgeDaysSinceCreationNameHashDocIDHashAverageLeadTimeLodgingRevenueOtherRevenueBookingsCanceledBookingsNoShowedBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRHighFloorSRLowFloorSRAccessibleRoomSRMediumFloorSRBathtubSRShowerSRCribSRKingSizeBedSRTwinBedSRNearElevatorSRAwayFromElevatorSRNoAlcoholInMiniBarSRQuietRoom
111723111724ITA56.0370x82277FA4AD074DA9786E64E977C94FEF1C2E47CD621287F5F832E92057F7D16C0x5ED6055421E857EA3496D4C75FA2237ACD353DB1B64BE221A60C6F1C6EACAB2300.000.000000Travel Agent/OperatorOther1000000100000
111724111725ESP60.0370x0BD0E67519BA9C6A100F53BE45C8B5E4FDFE79CAB071B35BF6CCDA8010FB23970xF47AA4F8422F4922863FAA5F18E30E6F8A9981D1EE7C02A87837F7E62E68CDA643875.00167.8001105Travel Agent/OperatorOther1000000011000
111725111726PAN60.0370xAA3529E305173010BADE927C7015FA091C3883D257E94F029F86B02B16ABBFA80xDC47997245A7B9ACF9D4974149D2100CEB00981A2F039DC0E82B0759965DF49600.000.000000Travel Agent/OperatorOther1000000011000
111726111727PRT51.0370x438E070937F7AB205414A419AF5D17520D3E89C49960954CC2AE541EAD3031FD0xFBDAEBF917AF8A24541A54251297EB4DA9E1C40C3C9F53DE7035638857F5B9317173.5518.000111DirectDirect1000000000001
111727111728DEU34.0360x39BEF6C854451EC00FD7C79A8E1B3F8DFC40BDEEC217C25540CF5BA7A019FA510x563B66C0301693C2BAEEDFA2340820F3A51BAA895B1E0A65D8A4EDF786F903094198.0014.000121Travel Agent/OperatorTravel Agent/Operator0000000100000
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111729111730BRA43.0360x2708B9F11C95F384129152CDF0830B566F02D42B87ACADE235EC78DE898737200xE87DEB08B0D7D0BDC590949FF04AAA893018BD8EB714EE129D14F9E3D29A368C170755.2520.0001105Travel Agent/OperatorOther0000000100000
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